AI-Enhanced Question Paper Generator  using Ant Colony Optimization

Author(s)	Prof. Sanjay Kalamdhad, Mr. Sannidhya Bhaisare, Mr. Mandar Patne, Mr. Smit Bhusari, Mr. Vedant Kalamdhad
Country	India
Abstract	Abstract— Creating examination question papers is a time-consuming and inconsistent manual task. This paper proposes an AI-Enhanced Question Paper Generator that automates and optimizes this process using Generative AI and the Ant Colony Optimization (ACO) algorithm. Generative AI (via Google Gemini API) extracts and generates questions from any study material, eliminating the need for static question banks. The ACO algorithm then selects an optimal set of questions ensuring balanced difficulty, topic coverage, and marks distribution. This system reduces educators’ workload, enhances assessment quality, and offers a scalable, efficient solution for institutions.
Keywords	Question Paper Generation, Generative AI, Ant Colony Optimization, Google Gemini API, Automated Assessment, Educational Technology, Difficulty Balancing, Topic Coverage, Optimization Algorithm, Intelligent System
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-11-12
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9425
Short DOI	https://doi.org/hbbmzw

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International Journal on Science and Technology